Non-invasive methodologies for assessing the state of health of the heart, such as using a torso array of electrocardiographic (ECG) signals to localize cardiac activity has been of continuing interest. Present methods which approach this problem involve the processing of the ECG torso signals to provide a mapping of cardiac potentials from a particular weighting and processing of the signals, frequently referred to as finding a solution for the inverse problem.
Considered separately from the inverse problem is the forward problem, wherein a model is developed to predict the interaction of the signals produced on the surface of the heart through the section of the human torso to the surface thereof. Such forward solutions as derived from a geometric model of the human torso, prepared according to cross sections of a representative subject derived from imaging techniques such as CAT systems. However, the difficulty involved in representing complex anatomical structures in a mathematically useful form has led many investigators to include simplifying assumptions and including empirical results based on animal experiments. However, limitations in the resulting model, which may differ substantially from the actual subjects anatomy, will adversely affect the solution of the inverse problem. However, the field of electrophysiology, including electrocardiography, historically views the forward and inverse problems, and their solutions, as separate fields of endeavor having different investigators. Therefore, the models developed to solve the forward problem in electrocardiography include unresolved limitations. Furthermore, such models and inherent limitations are frequently not considered in the inverse solution.
Popular realizations of inverse solutions have been based on applications of least squares reconstruction principles as constrained (regularized) least squares (CLS) and singular value decomposition (SVD), which operate in the time-space domain of data. Although these methods offer the advantages of relative algorithm simplicity and the ability to reconstruct signal amplitude as well as shape, they are fairly sensitive to measurement noise and require a large number of body surface sensors to provide accurate results. Furthermore, they require a priori knowledge or an estimate of a parameter whose value can significantly distort the results. Such disadvantages create serious difficulties to successfully implement these methods of inverse solution in a clinical setting.
The above-mentioned limitations of popular methods of electrocardiography further limit their usefulness and accuracy in the detection localization and quantification of cardiac dysfunction, such as myocardial ischemia.